konieshadow / speaker-diarization

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{
  "audio": "https://r2.getcastify.com/lex_ai_john_carmack_1.wav"
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run konieshadow/speaker-diarization using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "konieshadow/speaker-diarization:c58b6b038f6de30f93eaccd6aecb59d1b9a48ac13b22be000bcffe853efb2c20",
  {
    input: {
      audio: "https://r2.getcastify.com/lex_ai_john_carmack_1.wav"
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

Run konieshadow/speaker-diarization using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "konieshadow/speaker-diarization:c58b6b038f6de30f93eaccd6aecb59d1b9a48ac13b22be000bcffe853efb2c20",
    input={
        "audio": "https://r2.getcastify.com/lex_ai_john_carmack_1.wav"
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run konieshadow/speaker-diarization using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "konieshadow/speaker-diarization:c58b6b038f6de30f93eaccd6aecb59d1b9a48ac13b22be000bcffe853efb2c20",
    "input": {
      "audio": "https://r2.getcastify.com/lex_ai_john_carmack_1.wav"
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/konieshadow/speaker-diarization@sha256:c58b6b038f6de30f93eaccd6aecb59d1b9a48ac13b22be000bcffe853efb2c20 \
  -i 'audio="https://r2.getcastify.com/lex_ai_john_carmack_1.wav"'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/konieshadow/speaker-diarization@sha256:c58b6b038f6de30f93eaccd6aecb59d1b9a48ac13b22be000bcffe853efb2c20
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "audio": "https://r2.getcastify.com/lex_ai_john_carmack_1.wav"
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

output.json

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20.4 seconds

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